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From Paul Libbrecht <p...@hoplahup.net>
Subject Re: Retrieving large numbers of documents from several disks in parallel
Date Thu, 22 Dec 2011 07:11:22 GMT
Michael,

from a physical point of view, it would seem like the order in which the documents are read
is very significant for the reading speed (feel the random access jump as being the issue).

You could:
- move to ram-disk or ssd to make a difference?
- use something different than a searcher which might be doing it better (pure speculation:
does a hit-collector make a difference?)

hope it helps.

paul


Le 22 déc. 2011 à 03:45, Robert Bart a écrit :

> Hi All,
> 
> 
> I am running Lucene 3.4 in an application that indexes about 1 billion
> factual assertions (Documents) from the web over four separate disks, so
> that each disk has a separate index of about 250 million documents. The
> Documents are relatively small, less than 1KB each. These indexes provide
> data to our web demo (http://openie.cs.washington.edu), where a typical
> search needs to retrieve and materialize as many as 3,000 Documents from
> each index in order to display a page of results to the user.
> 
> 
> In the worst case, a new, uncached query takes around 30 seconds to
> complete, with all four disks IO bottlenecked during most of this time. My
> implementation uses a separate Thread per disk to (1) call
> IndexSearcher.search(Query query, Filter filter, int n) and (2) process the
> Documents returned from IndexSearcher.doc(int). Since 30 seconds seems like
> a long time to retrieve 3,000 small Documents, I am wondering if I am
> overlooking something simple somewhere.
> 
> 
> Is there a better method for retrieving documents in bulk?
> 
> 
> Is there a better way of parallelizing indexes from separate disks than to
> use a MultiReader (which doesn’t seem to parallelize the task of
> materializing Documents)
> 
> 
> Any other suggestions? I have tried some of the basic ideas on the Lucene
> wiki, such as leaving the IndexSearcher open for the life of the process (a
> servlet). Any help would be greatly appreciated!
> 
> 
> Rob


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